An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks

Researchers propose an efficient unsupervised federated learning framework for anomaly detection in heterogeneous IoT networks that preserves privacy while leveraging shared features from multiple datasets. The approach uses explainable AI techniques like SHAP for transparency and demonstrates superior performance compared to conventional federated learning methods on real-world IoT datasets.